remove max pooling from models for better infromation flow
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@ -1,6 +1,7 @@
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import keras
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from keras.engine import Input, Model
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from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, TimeDistributed, MaxPool1D
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from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, TimeDistributed, MaxPool1D, \
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GlobalAveragePooling1D
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def get_embedding(vocab_size, embedding_size, input_length,
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@ -8,12 +9,13 @@ def get_embedding(vocab_size, embedding_size, input_length,
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x = y = Input(shape=(input_length,))
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y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
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y = Conv1D(filter_size, kernel_size=5, activation='relu')(y)
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y = MaxPool1D(pool_size=3, strides=1)(y)
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# NOTE: max pooling destroys information flow for embedding
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# y = MaxPool1D(pool_size=3, strides=1)(y)
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y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
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y = MaxPool1D(pool_size=3, strides=1)(y)
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# y = MaxPool1D(pool_size=3, strides=1)(y)
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y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
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y = GlobalMaxPooling1D()(y)
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y = Dropout(drop_out)(y)
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y = GlobalAveragePooling1D()(y)
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# y = Dropout(drop_out)(y)
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y = Dense(hidden_dims, activation="relu")(y)
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return Model(x, y)
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